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Compressed prompts aid instruction-tuned language models (LMs) in overcoming context window limitations and reducing computational costs.
Language models are few-shot learners
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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Policy gradient methods for reinforcement learning with function approximation
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Automatic evaluation of summaries using n-gram co-occurrence statistics
Lin, C.-Y.; and Hovy, E. 2003 · 2003
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Natural language processing with Python: analyzing text with the natural language toolkit
Bird, S.; Klein, E.; and Loper, E. 2009 · 2009
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Contextual multi-armed bandits
Lu, T.; Pál, D.; and Pál, M. 2010 · 2010
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Reinforcement learning with deep energy-based policies
Haarnoja, T.; Tang, H.; Abbeel, P.; and Levine, S. 2017 · 2017
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Self-critical sequence training for image captioning
Rennie, S.; Marcheret, E.; Mroueh, Y.; Ross, J.; and Goel, V. 2017 · 2017
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Ranking sentences for extractive summarization with reinforcement learning
Narayan, S.; Cohen, S. B.; and Lapata, M. 2018 · 2018
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Simple Unsupervised Summarization by Contextual Matching
Zhou, J.; and Rush, A. M. 2019 · 2019
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The Summary Loop: Learning to Write Abstractive Summaries Without Examples
Laban, P.; Hsi, A.; Canny, J.; and Hearst, M. A. 2020 · 2020
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Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction
Schumann, R.; Mou, L.; Lu, Y.; Vechtomova, O.; and Markert, K. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Shin, T.; Razeghi, Y.; Logan IV, R. L.; Wallace, E.; and Singh, S. 2020 · 2020
Cited alongside, same era.
Keep it simple: Unsupervised simplification of multi-paragraph text
Laban, P.; Schnabel, T.; Bennett, P.; and Hearst, M. A. 2021 · 2021
Cited alongside, same era.
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
Cited alongside, same era.
Liu, X.; Zheng, Y.; Du, Z.; Ding, M.; Qian, Y.; Yang, Z.; and Tang, J. 2021 · 2021
Cited alongside, same era.
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
Schick, T.; and Schütze, H. 2021 · 2021
Cited alongside, same era.
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models
Wingate, D.; Shoeybi, M.; and Sorensen, T. 2022 · 2022
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Tempera: Test-time prompt editing via reinforcement learning
Zhang, T.; Wang, X.; Zhou, D.; Schuurmans, D.; and Gonzalez, J. E. 2022 · 2022
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Large Language Models are Human-Level Prompt Engineers
Zhou, Y.; Muresanu, A. I.; Han, Z.; Paster, K.; Pitis, S.; Chan, H.; and Ba, J. 2022 · 2022
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Falcon-40B: an open large language model with state-of-the-art performance
Almazrouei, E.; Alobeidli, H.; Alshamsi, A.; Cappelli, A.; Cojocaru, R.; Debbah, M.; Goffinet, E.; Heslow, D.; Launay, J.; Malartic, Q.; et al. 2023 · 2023
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Adapting Language Models to Compress Contexts
Chevalier, A.; Wettig, A.; Ajith, A.; and Chen, D. 2023 · 2023
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Finetuned Language Models are Zero-Shot Learners
Wei, J.; Bosma, M.; Zhao, V.; Guu, K.; Yu, A. W.; Lester, B.; Du, N.; Dai, A. M.; and Le, Q. V. 2021 · 2021
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, E.; Wang, X.; Dehghani, M.; Brahma, S.; et al. 2022 · 2022
Cited alongside, same era.
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
Deng, M.; Wang, J.; Hsieh, C.-P.; Wang, Y.; Guo, H.; Shu, T.; Song, M.; Xing, E.; and Hu, Z. 2022 · 2022
Cited alongside, same era.
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Ghalandari, D.; Hokamp, C.; and Ifrim, G. 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
Cited alongside, same era.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V.; Webson, A.; Raffel, C.; Bach, S. H.; Sutawika, L.; Alyafeai, Z.; Chaffin, A.; Stiegler, A.; Le Scao, T.; Raja, A.; et al. 2022 · 2022
Cited alongside, same era.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Wang, Y.; Mishra, S.; Alipoormolabashi, P.; Kordi, Y.; Mirzaei, A.; Naik, A.; Ashok, A.; Dhanasekaran, A. S.; Arunkumar, A.; Stap, D.; et al. 2022b · 2022
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Chatgpt outperforms crowd-workers for text-annotation tasks
Gilardi, F.; Alizadeh, M.; and Kubli, M. 2023 · 2023
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Huang, F.; Kwak, H.; and An, J. 2023 · 2023
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Li, Y. 2023 · 2023
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Learning to compress prompts with gist tokens
Mu, J.; Li, X. L.; and Goodman, N. 2023 · 2023
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GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
Prasad, A.; Hase, P.; Zhou, X.; and Bansal, M. 2023 · 2023
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Stanford Alpaca: An Instruction-following LLaMA model
Taori, R.; Gulrajani, I.; Zhang, T.; Dubois, Y.; Li, X.; Guestrin, C.; Liang, P.; and Hashimoto, T. B. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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Is chatgpt a good nlg evaluator? a preliminary study
Wang, J.; Liang, Y.; Meng, F.; Shi, H.; Li, Z.; Xu, J.; Qu, J.; and Zhou, J. 2023 · 2023
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